Lukkunaprasit et al. BMC Medical Genetics
https://doi.org/10.1186/s12881-020-01147-2(2020) 21:210

RESEARCH ARTICLE

Open Access

The association between genetic
polymorphisms in ABCG2 and SLC2A9 andurate: an updated systematic review andmeta-analysis
Thitiya Lukkunaprasit1,2, Sasivimol Rattanasiri1* , Saowalak Turongkaravee3, Naravut Suvannang1,
Atiporn Ingsathit1, John Attia4 and Ammarin Thakkinstian1

Abstract
Background: Replication studies showed conflicting effects of ABCG2 and SLC2A9 polymorphisms on gout andserum urate. This meta-analysis therefore aimed to pool their effects across studies.
Methods: Studies were located from MEDLINE and Scopus from inception to 17th June 2018. Observational studies
in adults with any polymorphism in ABCG2 or SLC2A9, and outcome including gout, hyperuricemia, and serum
urate were included for pooling. Data extractions were performed by two independent reviewers. Genotype effects
were pooled stratified by ethnicity using a mixed-effect logistic model and a multivariate meta-analysis fordichotomous and continuous outcomes.
Results: Fifty-two studies were included in the analysis. For ABCG2 polymorphisms, mainly studied in Asians,
carrying 1–2 minor-allele-genotypes of rs2231142 and rs72552713 were respectively about 2.1–4.5 and 2.5–3.9 times
higher odds of gout than non-minor-allele-genotypes. The two rs2231142-risk-genotypes also had higher serum
urate about 11–18 μmol/l. Conversely, carrying 1–2 minor alleles of rs2231137 was about 36–57% significantly lower
odds of gout. For SLC2A9 polymorphisms, mainly studied in Caucasians, carrying 1–2 minor alleles of rs1014290,
rs6449213, rs6855911, and rs7442295 were about 25–43%, 31–62%, 33–64%, and 35–65% significantly lower odds ofgout than non-minor-allele-genotypes. In addition, 1–2 minor-allele-genotypes of the latter three polymorphisms
had significantly lower serum urate about 20–49, 21–51, and 18–54 μmol/l than non-minor-allele-genotypes.
Conclusions: Our findings should be useful in identifying patients at risk for gout and high serum urate and thesepolymorphisms may be useful in personalized risk scores.
Trial registration: PROSPERO registration number: CRD42018105275.
Keywords: ABCG2, Gout, Hyperuricemia, Meta-analysis, Single nucleotide polymorphism, SLC2A9, Urate* Correspondence: sasivimol.rat@mahidol.edu
1
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine,
Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi,
Bangkok 10400, Thailand
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
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Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

Introduction
Hyperuricemia, defined as serum urate > 7 mg/dl (or416.4 μmol/l) [1], can lead to gout [1] and increased riskof renal disease, diabetes, hypertension, and cardiovascular disease [2]. Genome-wide association studies(GWAS) have shown that many single nucleotide polymorphisms (SNPs) of ATP-binding cassette sub-family
G member 2 gene (ABCG2) and the solute carrier family
2 member 9 (SLC2A9) are associated with serum urate
and gout [3–6]. Both ABCG2 and SLC2A9 are locatedon chromosome 4 [7]. Many more individual studies
have replicated the findings of GWASs and these weresummarized in systematic reviews of the effects of
ABCG2 [8–13] and SLC2A9 [12, 14–16] polymorphisms
on gout. The most updated review of ABCG2 [12] included 12 individual studies, indicating that carrying the
A allele of rs2231142 increased the risk of gout by about
1.8–2.3 times relative to the C allele in Asian and nonAsian populations. The most recent review of SLC2A9[15] included 13 studies indicating that rs6449213 (Cversus T), rs16890979 (T versus C) and rs1014290 (Cversus T) significantly decreased the risk of gout whereas
rs3733591 increased the risk of gout only in Asian andMaori populations and Solomon Islanders.
There have been 12 studies of ABCG2-rs2231142 on
gout published after these reviews and most reviews did
not pool genotype effects, which could lead to suggest
mode of gene effects. In addition, the effects of ABCG2and SLC2A9 polymorphisms on serum urate have neverbeen systematically reviewed. Therefore, an updated systematic review and meta-analysis regarding the influenceof SNPs in ABCG2 and SLC2A9 on gout, hyperuricemia
and serum urate is needed.
Methods
Search terms and strategies

Studies were located from MEDLINE (via PubMed
search engine) and Scopus databases from inception to
17th June 2018. The search terms and strategies wereconstructed based on study genes (i.e., ABCG2 and
SLC2A9 genes) and outcomes (i.e., gout, hyperuricemia
and serum urate); see more details in Additional file 1.
This study was registered in PROSPERO number
CRD42018105275.

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Data extraction

Two of 3 authors (i.e., TL, SR, ST, and NS) independently extracted data. Extracted information included
study characteristics (study setting, study design), participants (ethnicity, age, gender, body mass index (BMI),
estimated glomerular filtration rate (eGFR), comorbidity, medications, and alcohol consumption),
genes (ABCG2/SLC2A9/both, allele/genotype, and frequency/summary data), and outcomes (gout and its
diagnostic criteria, hyperuricemia and its definition, andserum urate). In addition, data for pooling (frequency
data for allele/genotype and outcome, and mean andstandard deviation (SD) of urate by allele/genotype) werealso extracted. Missing data was requested from authors.
Any disagreement was resolved by discussion andconsensus.
Risk of bias assessment

Risk of bias was independently assessed by two reviewers(TL and SR) using a risk-of-bias tool [17] which consisted of 4 domains, i.e., information bias, confounding
bias, selective reporting, and Hardy-Weinberg equilibrium (HWE). Each question was answered as yes, no,and unclear representing low/no, possible/high, and unclear risk of bias due to insufficient information, respectively. Any disagreement was resolved by discussion andconsensus with the team.
Study genes and outcome of interest

Data for the following polymorphisms were collected:
ABCG2 gene (rs2231142 C > A, rs72552713 C > T andrs2231137 C > A) and SLC2A9 gene (rs1014290 G > A,
rs3733591 G > A, rs6449213 G > A, rs6855911 G > A,
rs7442295 G > A, rs12510549 G > A, rs16890979 G > A,and rs734553 T > G). Outcomes of interest were gout,
hyperuricemia, and serum urate defined according tooriginal studies.
Statistical analysis

Statistical analysis was performed using methods previously described [18]. HWE was checked using an exact
test and only studies which complied with HWE wereconsidered in the analysis. Prevalence of the minor alleleof each SNP was pooled, and stratified by ethnicity.
Gene effects were assessed as follows:

Inclusion/exclusion criteria

Per-allele approach

Screening of titles and abstracts were performed by TL
and randomly checked by SR. Any type of observational
study was selected as follows: studies in adults, with any
polymorphism in ABCG2 or SLC2A9, and any outcome
including gout, hyperuricemia, and serum urate. Studies
with insufficient data and unavailable full-texts were
excluded.

For hyperuricemia and gout, allele effect (i.e., odds ratio(OR)) for minor allele a versus A along with 95% confidence interval (CI) was estimated. Mean difference(MD) of urate, in μmol/l, for a versus A was estimated.
Heterogeneity was explored using Cochrane’s Q test andI2. A random-effect model was used for pooling ORs if
heterogeneity was present (P-value < 0.10 or I2 ≥ 25%),

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

otherwise a fixed-effect model was applied. Sources ofheterogeneity (e.g. age, gender, co-morbidity, etc.) were
explored using meta-regression if the data were available, and subgroup analyses were then performed
accordingly.
Per-genotype approach

ORs (i.e., OR1 (aa versus AA) and OR2 (Aa versus AA))and mean differences (MDs) (MD1 (aa versus AA) andMD2 (Aa versus AA)) were estimated for dichotomousand continuous outcomes, respectively. Heterogeneity
was explored as above. Aggregated genotype and outcome data were expanded to individual patient data(IPD). A one-stage approach with a mixed-effect logistic
regression was applied by fitting genotypes (i.e., aa versus AA and Aa versus AA) on outcome, and pooled ORs
were estimated. For serum urate, a multivariate (mv)meta-analysis was applied to pool MD1 and MD2 acrossstudies. Mode of gene effects (i.e., dominant, recessive,
additive) was then determined by estimating lambda (λ)(i.e., logOR2/logOR1 or MD2/MD1) using model-free
Bayesian approach [19].
Sensitivity analysis was performed by including studies
with HWE disequilibrium. Publication bias was assessedusing funnel plots and Egger’s tests. If any of these suggested asymmetry, a contour enhanced-funnel was constructed to determine whether asymmetry was caused bypublication bias or heterogeneity.
STATA software version 15.1 and WinBUGS version
1.4.3 were used for analyses. The level of significancewas < 0.05 except for the heterogeneity test, in which <0.10 was used.

Results
Identifying studies

Among 2087 identified studies, 52 studies were eligible,
which included one study manually added from the reference, see Fig. 1. Among them, 34, 5, and 22 studies
had outcomes of gout, hyperuricemia, and serum urate,
respectively.
The characteristics of 52 studies (i.e., 30 ABCG2 studies [20–49] and 32 SLC2A9 studies [24–26, 28, 31, 33,
39, 40, 43, 45, 50–71]) with a total of 63 sub-studies aredescribed in Additional file 2. Most studies were cohorts(50%), and split roughly evenly between Asians (46%)
and Caucasians (42%). The mean ages and BMIs ranged
from 37 to 66 years and from 22.6 to 34.6 kg/m2, respectively. Of 34 studies on gout, most studies (68%)
used the criteria of American College of Rheumatology[72] for diagnosis. All studies used the cut-off of
416.4 μmol/l (or 7 mg/dl) for defining hyperuricemia.

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Risk of bias assessment

The risk of bias was low for population stratification andselective outcome report (see Additional file 3 A-B).
Twenty sub-studies were at high risk of bias in ascertainment of genotyping because they did not clearly describe
the methods they used. Furthermore, 17 sub-studies hadhigh risk of bias in ascertainment of outcome mostly
due to no information clearly mentioned.

Gout

Among 34 studies, 3 SNPs in ABCG2 (i.e., rs2231142,
rs72552713, and rs2231137) and 7 SNPs in SLC2A9 (i.e.,
rs1014290,
rs3733591,
rs6449213,
rs16890979,
rs6855911, rs7442295, and rs12510549) had sufficient
data for pooling as described in Additional file 4.1.
Minor allele prevalences of these polymorphisms werepooled, see Additional file 4.2.
Genotype effects of ABCG2 polymorphisms (i.e.,
rs2231142 (N = 21), rs72552713.
(N = 7), and rs2231137 (N = 6)) were estimated stratifying by Asians and Caucasians, see Fig. 2, Table 1, andAdditional file 4.1. In Asian populations, carrying thehomozygous minor and heterozygous genotypes of
rs2231142 and rs72552713 were higher risk of gout thanthe homozygous major genotypes with pooled OR1 andOR2 of 4.53 (4.10, 5.00) and 2.10 (1.95, 2.26) for
rs2231142; and 3.86 (2.30, 9.76) and 2.46 (1.93, 3.18) for
rs72552713. Sensitivity analysis by including a study that
did not comply with HWE for rs2231142 [42] did notchange the results (data not shown). Likewise, rs2231142
also carried higher risk of gout in Caucasians withpooled OR1 and OR2 of 3.24 (2.39, 4.41) and 1.64 (1.47,
1.82). Conversely, carrying homozygous minor and heterozygous genotypes of rs2231137 carried lower risk ofgout in Asians with pooled OR1 and OR2 of 0.43 (0.34,
0.55) and 0.64 (0.56, 0.72), respectively.
Effects of 7 polymorphisms in SLC2A9 (i.e., rs1014290(N = 7), rs3733591 (N = 7), rs6449213 (N = 5), rs6855911(N = 5), rs16890979 (N = 4), rs7442295 (N = 3), andrs12510549 (N = 3)) on gout were assessed, see Fig. 2,
Table 1, and Additional file 4.1. Pooled minor allele
prevalences are reported in Additional file 4.2. Among
Asian studies, effects of 3 polymorphisms (i.e.,
rs3733591, rs1014290, and rs6855911) on gout werepooled indicating that homozygous minor and heterozygous genotypes of rs3733591 carried higher risks of gout,i.e. 1.55 (1.17, 2.06) and 1.29 (1.07, 1.55) times higher
than homozygous major genotype. Conversely, carrying
homozygous minor and heterozygous genotypes of
rs1014290 and rs6855911 carried lower risk, but only
the former SNP was significant with pooled OR1 andOR2 of 0.42 (0.36, 0.49) and 0.65 (0.58, 0.72),
respectively.

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

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Fig. 1 Flow for identifying and selecting studies. ABCG2, ATP-binding cassette sub-family G member 2; SLC2A9, solute carrier family 2 member 9

Among these SLC2A9 polymorphisms, only rs1014290
and rs6855911 were significantly associated with gout in
Caucasians with pooled OR1 and OR2 for homozygous
minor and heterozygous genotypes of 0.57 (0.37, 0.87)

and 0.75 (0.63, 0.89) for rs1014290, and 0.36 (0.28, 0.46)
and 0.67 (0.59, 0.77) for rs6855911; the effects ofrs3733591 were not significant. Three additional polymorphisms, studied only in Caucasians, were also

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

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Fig. 2 Summary of pooled genotype effects on gout. a Increasing risk effects b Decreasing risk effects. ABCG2, ATP-binding cassette sub-family G
member 2; CI, confidence interval; OR, odds ratio; SLC2A9, solute carrier family 2 member 9; SNP, single nucleotide polymorphism

significantly associated with gout, i.e., rs6449213,
rs7442295, and rs12510549. Homozygous minor andheterozygous genotypes of these polymorphisms carried
lower risk than homozygous major genotype, withpooled OR1 and OR2 of 0.38 (0.29, 0.50) and 0.69 (0.62,0.78) for rs6449213; 0.35 (0.28, 0.46) and 0.65 (0.57,0.74) for rs7442295; and 0.44 (0.34, 0.58) and 0.66 (0.58,0.76) for rs12510549, see Fig. 2, Table 1, andAdditional file 4.1.
The mode of gene effects (λ) were estimated suggesting that effects of ABCG2 polymorphisms were mostly
additive effects, except for rs72552713 which might be
between an additive or dominant effect, see Table 1.
Likewise, the mode of SLC2A9 effects on gout might bemostly additive effects, see Table 1.
Effects of ABCG2 and SLC2A9 polymorphisms on gout
were homogenous to highly heterogeneous with I2 valuesof 0 to 75.2% and 39.2 to 62.9% in Asians and Caucasians for ABCG2; and 0 to 68.1% and 0 to 49.4% in
Asians and Caucasians for SLC2A9 polymorphisms, see
Table 1. Sources of heterogeneity were explored for
ABCG2 polymorphisms (i.e., rs2231142 and rs2231137),
see Additional file 4.3. Sub-group analysis by percent

male ≥90% versus < 90% indicated stronger effects particularly for OR1 in percent male ≥90% with the OR1 of
5.32 (4.75, 5.97) and 6.14 (0.72, 52.13) for rs2231142 in
Asians and Caucasians.
Likewise, sources of heterogeneity were explored for
SLC2A9 polymorphisms; again, percent male was a potential source of variation for rs1014290 and rs3733591
in Asians, and rs7442295 in Caucasians, see Additional file 4.3. Their effects were even higher in studies
with a high percent of males, with pooled OR1 of 0.39
(0.33, 0.46) for rs1014290 and 2.52 (1.71, 3.73) for
rs3733591 in Asians, and 0.19 (0.09, 0.41) for rs7442295
in Caucasians. Publication bias was assessed by Egger’s
tests and funnel plots, see Additional file 4.4–4.5, andnone of these polymorphisms had evidence of publication bias.
Hyperuricemia

Effects of rs2231142 (in ABCG2) on hyperuricemia wereassessed (N = 5, see Additional file 5.1). Carrying homozygous minor and heterozygous genotypes carried ahigher risk of hyperuricemia than carrying homozygous
major genotype with pooled OR1 and OR2 of 2.25 (1.80,

(2020) 21:210

Lukkunaprasit et al. BMC Medical Genetics

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Table 1 Summary of pooled effect sizes of ABCG2 and SLC2A9 polymorphisms on gout and hyperuricemia
Asian
Na I 2

Caucasian
Pooled OR 95% CI

Lambdab 95% CI

Na I2

Pooled OR 95% CI

Lambdab 95% CI

ABCG2
Gout
rs2231142
OR1 (AA versus CC)

16 75.2

4.53

4.10, 5.00 0.463

OR2 (CA versus CC)

16 68.7

2.10

1.95, 2.26

0.332, 0.616 5

39.2 3.24

2.39, 4.41 0.461

562.9 1.64

1.47, 1.82

0.317, 0.847 3

0.0

0.57

0.37, 0.87 0.474

3

0.0

0.75

0.63, 0.89

0.087, 0.960 3

0.0

1.03

0.60, 1.79 0.463

0.253, 0.883

rs72552713
OR1 (TT versus CC)

8

0.0

3.86

2.30, 9.76 0.670

OR2 (CT versus CC)

8

0.0

2.46

1.93, 3.18

0.362, 0.978

rs2231137
OR1 (AA versus GG) 6

54.6 0.43

0.34, 0.55 0.547

OR2 (GA versus GG) 6

54.9 0.64

0.56, 0.72

0.262, 0.925

Hyperuricemia
rs2231142
OR1 (AA versus CC)3

67.2 2.25

1.80, 2.81 0.527

OR2 (CA versus CC)3

44.6 1.56

1.35, 1.80

OR1 (CC versus TT)

655.0 0.42

0.36, 0.49 0.520

OR2 (TC versus TT)

60.0

0.58, 0.72

0.131, 0.940

SLC2A9Gout
rs1014290

0.65

0.092, 0.954

rs3733591
OR1 (CC versus TT)

668.1 1.55

1.17, 2.06 0.512

OR2 (TC versus TT)645.2 1.29

1.07, 1.55

3

28.8 0.85

0.48, 1.50

OR1 (CC versus TT)

529.0 0.38

0.29, 0.50 0.400

OR2 (TC versus TT)

50.0

0.62, 0.78

0.021, 0.972

rs6449213

0.69

0.179, 0.829

rs6855911
OR1 (GG versus AA) 3

0.0

0.61

0.20, 1.87 0.395

OR2 (AG versus AA) 3

0.0

0.82

0.53, 1.27

0.019, 0.963 2

40.0 0.36

0.28, 0.46 0.370

249.4 0.67

0.59, 0.77

OR1 (GG versus AA)

3

38.7 0.35

0.28, 0.46 0.401

OR2 (AG versus AA)

3

8.6

0.65

0.57, 0.74

OR1 (CC versus TT)

3

0.0

0.44

0.34, 0.58 0.528

OR2 (TC versus TT)

3

8.5

0.66

0.58, 0.76

0.078, 0.872

rs16890979
OR1 (TT versus CC)

3

NAc NAc

OR2 (CT versus CC)

3

0.0

0.60

0.39, 0.94

rs7442295
0.163, 0.799

rs12510549
0.216, 0.942

ABCG2 ATP-binding cassette sub-family G member 2, CI confidence interval, NA not applicable, OR odds ratio, SLC2A9 solute carrier family 2 member 9aNumber of sub-studiesbMedian lambdacUnable to pool OR1 for rs16890979 due to no gout case with TT genotype foundLukkunaprasit et al. BMC Medical Genetics(2020) 21:210

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Fig. 3 Summary of pooled genotype risk effects on hyperuricemia. ABCG2, ATP-binding cassette sub-family G member 2; CI, confidence interval;
OR, odds ratio; SNP, single nucleotide polymorphism

2.81) and 1.56 (1.35, 1.80), respectively, see Fig. 3, Table 1,
and Additional file 5.1. The estimated λ was 0.527, suggesting an additive gene effect, see Table 1. Sensitivity analysis by including 1 Asian study that did not comply with
HWE [42] did not materially change the results (data notshown). These gene effects were moderately heterogeneous (I2 = 44.6–67.2%), with BMI and percent male aspotential sources, see Additional file 5.2. There was noevidence of publication bias from Egger’s tests and funnelplots, see Additional file 5.3–5.4.

Fig. 4 Summary of pooled genotype effects on mean difference of serum urate. a Increasing risk effects b Decreasing risk effects. ABCG2, ATPbinding cassette sub-family G member 2; CI, confidence interval; MD, mean difference; SLC2A9, solute carrier family 2 member 9; SNP, single
nucleotide polymorphism

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

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respectively in Asians, and − 32.33 (− 52.61,-12.05)
and − 9.87 (− 25.27,5.54), respectively in Caucasians,
see Fig. 4, Table 2, and Additional file 6.1.
Four additional SLC2A9 polymorphisms were studiedin Caucasians only. Homozygous minor and heterozygous genotypes of these SNPs reduced urate levels compared to homozygous major genotypes, with pooled
MD1 and MD2 of − 49.29 (− 59.85, − 38.74) and − 20.43(− 28.91, − 11.94) for rs6449213; − 51.23 (− 57.22, −45.25) and − 20.65 (− 23.83, − 17.47) for rs6855911; −54.30 (− 58.78, − 49.82) and − 54.30 (− 58.78, − 49.82) for
rs7442295; − 29.29 (− 63.90, 5.33) and − 20.68 (− 47.08,
5.71) for rs734553, see Fig. 4, Table 2, and Additional file 6.1. The mode of effects of these SLC2A9SNPs were most likely to be an additive effect, see
Table 2.
Effects of ABCG2 and SLC2A9 SNPs on serum urate
were homogeneous to highly heterogeneous, i.e., I2
ranged from 59.1 to 63.1% and 0 to 68.7% in Asians andCaucasians for ABCG2-rs2231142; 65.7 to 67.9% and 0
to 88.5% in Asians and Caucasians for SLC2A9 SNPs,

Serum urate

Among 22 studies, 1 SNP in ABCG2 (i.e., rs2231142)
and 5 SNPs in SLC2A9 (i.e., rs1014290, rs6449213,
rs6855911, rs7449253, and rs734553) had sufficient
data for pooling, see Additional file 6.1. An additional
polymorphism not studied for gout was rs734553 andthe pooled minor allele prevalence was 0.30 (0.28,
0.32). For rs2231142 (in ABCG2), homozygous minor
and heterozygous genotypes had higher urate levels
than homozygous major genotype, with pooled MD1
and MD2 of 18.41 (8.68, 28.14) and 11.49 (4.10,
18.89) respectively in Asians; and 50.43 (32.66, 68.20)
and 19.46 (14.19, 24.73) respectively in Caucasians,
see Fig. 4, Table 2, and Additional file 6.1. Sensitivity
analysis by including 1 Asian study that did not comply with HWE [39] did not materially change the results (data not shown). In contrast, homozygous
minor and heterozygous genotypes of rs1014290 (inSLC2A9) decreased urate more than homozygous
major genotype, with pooled MD1 and MD2 of −21.47 (− 30.97, − 11.96) and − 8.16 (− 16.63, 0.31),

Table 2 Summary of pooled effect sizes of ABCG2 and SLC2A9 polymorphisms on serum urate
Asian
N

a

I

2

Caucasian
Pooled 95% CI
MD

b

Na I2

Lambda

95% CI

0.605

0.284, 0.956 4

Pooled 95%CI
MD

Lambdab 95% CI

ABCG2Serum urate
rs2231142
MD1 (AA versus CC) 5

63.1 18.41

8.68, 28.14

MD2 (CA versus CC) 5

59.1 11.49

4.10, 18.89

68.7 50.43

32.66, 68.20

0.0

19.46

14.19, 24.73

0.411

0.197, 0.783

0.088, 0.856 3

86.9 −32.33−52.61,-12.05

0.420

0.087, 0.932

375.1 −9.87−25.27,5.54

MD1 (CC versus TT)
MD2 (TC versus TT)6

88.5 −49.29−59.85, −38.74 0.375

0.239, 0.622

6

26.0 −20.43−28.91, −11.94

MD1 (GG versus AA)

6

0.0−51.23−57.22, −45.25 0.399

MD2 (AG versus AA)6

0.0−20.65−23.83, −17.47

4

SLC2A9Serum urate
rs1014290
MD1 (CC versus TT)6

67.9 −21.47−30.97, −11.96 0.381

MD2 (TC versus TT)6

65.7 −8.16−16.63, 0.31

rs6449213

rs6855911
0.316, 0.488

rs7442295
MD1 (GG versus AA)

6

0.0 −54.30−58.78, − 49.82 0.326

MD2 (AG versus AA)

6

26.4 −18.34−22.68, −13.99

MD1 (GG versus TT)380.6 −29.29−63.90, 5.33

MD2 (TG versus TT)352.8 −20.68−47.08, 5.71

0.280, 0.426

rs734553
0.592

ABCG2 ATP-binding cassette sub-family G member 2, CI confidence interval, MD mean difference, SLC2A9 solute carrier family 2 member 9aNumber of sub-studiesbMedian lambda

0.152, 0.969

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

see Table 2. In Caucasians, type of population accounted
for some of the heterogeneity, see Additional file 6.2.
After excluding one Caucasian study in type 2 diabetes(T2D) patients, sub-group analysis yielded stronger effects of these SNPs in a general population with pooled
MD1 and MD2 of − 40.16 (− 47.74, − 32.57) and − 15.80(− 23.36, − 8.24) for rs1014290; and − 55.67 (− 82.77, −28.57)) and − 29.00 (− 41.82, − 16.19) for rs734553. The
effects of rs734553 became significant and strongest
after excluding the study in the T2D population. Publication bias was assessed by Egger’s tests and funnelplots, see Additional file 6.3–6.4. Publication bias may
be present for the effects of rs6449213 in Caucasians, assuggested by asymmetry of the funnel and the contour
enhanced-funnel plot (data not shown). The pooled effect sizes of ABCG2 and SLC2A9 SNPs on all outcomesare summarized in Table 3.

Discussion
We conducted a systematic review and meta-analysis toassess associations between SNPs in ABCG2 and
SLC2A9 and gout, hyperuricemia and serum urate,
stratified by ethnicity. ABCG2-SNPs were common inboth Asians and Caucasians with a minor allele frequency of 11 to 31%, except for rs72552713 which wasvery rare in Asians. SLC2A9-SNPs were common in both
Asians (6–41%) and Caucasians (18–40%), except for
rs6855911 and rs16890979 which were rare in Asians(1–6%). For ABCG2, rs2231142 significantly increased
risk of gout by about 2 to 4 times and 1.6 to 3 times in
Asians and Caucasians respectively for carrying heterozygous and homozygous minor genotypes. In addition,
carrying homozygous/heterozygous minor genotypes of
rs72552713 also increased risk of gout by about 2.5–3.9
times in Asians. By contrast, carrying homozygous
minor or heterozygous genotypes of rs2231137 reduced
risk of gout by about 36–57% in Asians. Likewise, most
SLC2A9-SNPs (i.e., rs1014290, rs6449213, rs6855911,
rs7442295, and rs12510549) showed significantly lower
risk of gout by about 25–65% in Asians and/or Caucasians, except for rs6855911 which was not significant in
Asians. Carrying homozygous minor or heterozygous genotypes of rs2231142-ABCG2 also increased the risk ofhyperuricemia in Asians and serum urate in both Asians
and Caucasians. In addition, all SLC2A9-SNPs, except
rs1014290 and rs734553, also significantly increased
serum urate level.
Regarding ABCG2, risk effects of rs2231142 were consistently found on gout, hyperuricemia and increased
serum urate in Asians. The risk effects were strong ongout compared to weaker effects on hyperuricemia andmodest effects on urate level. There might be othermechanisms of rs2231142 that lead to gout occurrence
without raising serum urate level. In Caucasians, there

Page 9 of 13

were weaker risk effects of rs2231142 on gout, but stronger risk effects on serum urate.
For SLC2A9, rs6855911, rs6449213 and rs7442295
could significantly lower risk of gout (OR1 of 0.35–0.38
and OR2 of 0.65–0.69) and serum urate (MD1 of − 54.30
to − 49.29 and MD2 of − 20 .65 to − 18.34) in Caucasians. The causative SNP might be any of these 3 SNPs
because there was high linkage disequilibrium between
rs6449213 and rs7442295 (r2 = 0.88) [31]. rs1014290 also
lowered the risk of gout in Caucasians and Asians, andits effects on serum urate were significant in Asians andfor MD1 in Caucasians; the lack of significance in MD2
in Caucasians is likely due to the low numbers of included studies in the latter given similar risk effects.
The percentage of males might be a potential sourceof heterogeneity in the effects of ABCG2 and SLC2A9 on
gout, since the risk or protective effects were consistently stronger in men; this may indicate sex-specific differences in pathological mechanisms or in the handlingof urate. T2D might be another source of heterogeneity(for the outcome of serum urate); excluding the study
with T2D Caucasians [26], made the effects of
rs1014290 and rs734553 stronger in the remaining/general population.
Considering the effect of ABCG2 SNPs on gout, effects
of rs2231142 were consistent with the recent updated
meta-analyses that pooled allele [12] and genotype [11]effects. Our pooling had 12 additional studies and stratified by Asian and Caucasian ethnicity because of different allele frequencies across these populations. Our
results for SLC2A9 SNPs and gout were also generally
consistent with the recent updated meta-analysis, but wewere able to pool additional effects of rs1014290,
rs6449213 and rs7449213 in Caucasians and also
rs6855911 in Asians. Some previous reviews reported
only pooled allele effects [12, 15] and some reported
many different types of pooling effects [14, 16]. However, we are the first to report both pooled homozygous
and heterozygous effects of these SLC2A9 SNPs on gout.
Furthermore, we also explored the effects of rs72552713
on gout, rs2231142 on hyperuricemia, and SNPs of bothgenes on serum urate level. Risk of bias in ascertainmentof genotyping and outcome were observed. However,publication bias was not evidenced in most analyses.
The ABCG2 protein is a urate efflux transporter responsible for urate excretion [42, 46]. In vitro studies
showed that rs2231142 reduced ABCG2 protein expression [42, 73–75], ATPase activity [76] and the uratetransport [21, 42, 46] resulting in higher accumulationof urate whereas rs2231137 did not change the level of
ABCG2 protein expression [42, 73, 75] and the uratetransport activity [21, 42]. The proposed mechanism bywhich rs2231137 lowers the risk of gout is still unknown. The effect of rs72552713 on increasing the risk

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

Page 10 of 13

Table 3 Summary of pooled effect sizes of ABCG2 and SLC2A9 polymorphisms on all outcomesa
Ethnicity Gout

Hyperuricemia

Serum urate

Pooled 95% CI Pooled 95% CI Pooled 95% CI Pooled 95% CI Pooled 95% CI
OR1
OR2
OR1
OR2
MD1

Pooled 95% CI
MD2

4.53

4.10,
5.00

2.10

1.95,
2.26

18.41 8.68, 28.14

11.49 4.10, 18.89

Caucasian 3.24

2.39,
4.41

1.64

1.47,
1.82

50.43 32.66,
68.20

19.46 14.19,
24.73

Asian

3.86

2.30,
9.76

2.46

1.93,
3.18

Asian

0.43

0.34,
0.55

0.64

0.56,
0.72

Asian

0.42

0.36,
0.49

0.65

0.58,
0.72

−21.47 −30.97,−11.96−8.16 −16.63, 0.31

Caucasian 0.57

0.37,
0.87

0.75

0.63,
0.89

−32.33 −52.61,12.05−9.87 −25.27,5.54

Asian

1.55

1.17,
2.06

1.29

1.07,
1.55

Caucasian 1.03

0.60,
1.79

0.85

0.48,
1.50

Asian

0.61

0.20,
1.87

0.82

0.53,
1.27

Caucasian 0.36

0.28,
0.46

0.67

0.59,
0.77

−51.23 −57.22,−45.25−20.65 −23.83,−17.47

rs6442295

Caucasian 0.38

0.29,
0.50

0.69

0.62,
0.78

−49.29 −59.85,−38.74−20.43 −28.91,−11.94rs7442295

Caucasian 0.35

0.28,
0.46

0.65

0.57,
0.74

−54.30 −58.78,−49.82−18.34 −22.68,−13.99Caucasian 0.44

0.34,
0.58

0.66

0.58,
0.76

0.60

0.39,
0.94
−29.29 −63.90, 5.33

−20.68 −47.08, 5.71

ABCG2
rs2231142

Asian

rs72552713
rs2231137

2.25

1.80,
2.81

1.56

1.35,
1.80

SLC2A9rs1014290

rs3733591

rs6855911

rs12510549
Asian
rs16890979
rs734553

NAb

Caucasian

ABCG2 ATP-binding cassette sub-family G member 2, CI confidence interval, MD mean difference, NA not applicable, OR odds ratio, SLC2A9 solute carrier family 2
member 9aSignificant OR or MD in boldbUnable to pool OR1 for rs16890979 due to no gout case with homozygous minor genotype foundof gout is supported by the fact that it is a nonsense
variant causing a stop codon that prematurely ends protein translation of ABCG2; Matsuo et al. [42] also reported the absence of ABCG2 protein expression andthe almost entirely removed urate transport activity due
to rs72552713 in the functional analysis. It was also
found that the combination of rs2231142 andrs72552713 attributed to a higher risk of hyperuricemia
than other typical risk factors including obesity, alcohol
consumption and increasing age [77]. ABCG2 variants
were associated with gout/hyperuricemia in both Asians
and Caucasians [21, 78]. Particularly, individuals ofEuropean descent who carried ABCG2 variants had asignificantly earlier onset of gout/hyperuricemia and thefamily history of gout was significantly more frequent
among them [78]. These findings are supported by arecent study showing that pediatric-onset gout/hyperuricemia patients had higher minor allele frequency of
rs2231142 than those of adult-onset patients and normouricemic controls [79].
The glucose transporter 9 (GLUT9) protein, regulatedby the SLC2A9 gene, is a urate reuptake transporter [3].
It was discovered that SLC2A9 polymorphisms cause
renal hypouricemia type 2 by reducing the urate transport activity leading to lowering renal urate reabsorption
and lower serum urate [80–82]. Therefore, SLC2A9
could be another promising target for lowering serum
urate and the risk of gout. However, one study foundthat SLC2A9 variants including rs16890979 andrs3733591 did not significantly change the urate transport activity in Xenopus oocyte model [83]. Therefore,
the findings regarding the functional mechanisms

Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

underlying the GWAS-identified association of SLC2A9
variants on urate transport are still not consistent. Ourpooled results showed a trend of decreasing the risk ofgout and serum urate in most SLC2A9 variants, except
for the effect of rs3733591 on gout in Asians. More association and functional studies are required to confirm/
explain this. In addition, a recent GWAS found that
ABCG2, SLC2A9, ALDH2, and novel loci induce asymptomatic hyperuricemia into gout development [84]. Thisemphasizes the strong influence of ABCG2 and SLC2A9
variants on gout development and points out the importance of identifying high risk patients having thesegenetic variants.
Our study has some strengths. We pooled genotype effects stratified by ethnicity for different allele frequencies. We considered all relevant outcomes, i.e., gout,
hyperuricemia, and serum urate and mode of gene effects were estimated. Although some gene effects were
heterogeneous, we could identify that male gender anddiabetes might be potential sources of heterogeneity.
However, we could not avoid some limitations. Genotype frequencies in some studies [23, 24, 26, 27, 32, 34,
38, 48, 53, 56, 61, 67, 68, 70] were missing and thus were
estimated assuming HWE. Some of these SNPs were inhigh linkage disequilibrium, so haplotype effects should
be further assessed to determine which polymorphism
had the largest causal effect. Other important confounders (e.g. diuretic use, dietary intake and alcohol
consumption) of gout/urate should be considered inassessing any association, but these data were not available. Only a few number of studies conducted in African
Americans and other indigenous people, so further studies in these populations should be conducted.
In conclusion, our study suggested that the ABCG2rs2231142 SNP increased the risk of gout and serum
urate in both Asians and Caucasians. In addition,
ABCG2-rs72552713 also increased the risk of goutwhereas ABCG2-rs2231137 reduced the risk in Asians.
Furthermore, SLC2A9-SNPs (i.e., rs1014290, rs6449213,
rs6855911, rs7442295, and rs12510549) significantly
lowered the risk of gout in Asians and/or Caucasians.
Further studies in other populations especially African
Americans are still needed. Our findings may be helpfulin creating more accurate risk stratification models.

Page 11 of 13

Additional file 4. Gout. 4.1. Data used for pooling effects of ABCG2 and
SLC2A9 polymorphisms on gout. 4.2. Pooled prevalence of minor alleleof ABCG2 and SLC2A9 polymorphisms. 4.3. Exploring source ofheterogeneity for ABCG2 and SLC2A9 polymorphisms on gout.4.4. Egger’s
tests for ABCG2 and SLC2A9 polymorphisms on gout. 4.5. Funnel plots for
ABCG2 and SLC2A9 polymorphisms on gout. (DOCX 662 kb)
Additional file 5. Hyperuricemia. 5.1. Data used for pooling effects of
ABCG2-rs2231142 on hyperuricemia. 5.2. Exploring source ofheterogeneity for ABCG2-rs2231142 on hyperuricemia. 5.3. Egger’s tests
for ABCG2-rs2231142 on hyperuricemia. 5.4. Funnel plots of ABCG2rs2231142 on hyperuricemia in Asians. A) OR1 in Asians B) OR2 in Asians.
(DOCX 77 kb)
Additional file 6. Serum urate. 6.1. Data used for pooling meandifference of ABCG2 and SLC2A9 polymorphisms on serum urate. 6.2.
Exploring source of heterogeneity for ABCG2 and SLC2A9 polymorphisms
on serum urate. 6.3. Egger’s tests for ABCG2 and SLC2A9 polymorphisms
on serum urate. 6.4. Funnel plots for ABCG2 and SLC2A9 polymorphisms
on serum urate. (DOCX 412 kb)

Abbreviations
ABCG2: ATP-binding cassette sub-family G member 2; BMI: Body mass index;
CI: Confidence interval; eGFR: Estimated glomerular filtration rate;
GWAS: Genome-wide association study; HWE: Hardy-Weinberg equilibrium;
MD: Mean difference; OR: Odds ratio; SD: Standard deviation; SLC2A9: Solute
carrier family 2 member 9; SNP: Single nucleotide polymorphism; T2D: Type2 diabetes
Acknowledgments
The authors would like to thank Mr. Stephen Pinder for proof-reading thisarticle.
Authors’ contributions
TL designed the study, performed acquisition of data, analysis andinterpretation of data, and drafting the article. SR performed acquisition,
analysis and interpretation of data. ST and NS performed acquisition of data.
AI and JA performed interpretation of data. AT designed the study andperformed interpretation of data. All authors read and approved the final
manuscript.
Funding
This research received no specific grant from any funding agency in the
public, commercial or not-for-profit sectors.
Availability of data and materials
All data generated or analysed during this study are included in thispublished article and its supplementary information files.
Ethics approval and consent to participate
Ethics approval and consent to participate are not required for this study.
Consent for publication
Not applicableCompeting interests
The authors declare that they have no competing interests.

Supplementary information
The online version contains supplementary material available at https://doi.
org/10.1186/s12881-020-01147-2.
Additional file 1. Search strategies. (DOCX 26 kb)
Additional file 2. Characteristics of included studies investigating
associations between ABCG2 and SLC2A9 polymorphisms and urate.
(DOCX 146 kb)
Additional file 3. Risk of bias assessment. (DOCX 26 kb)

Author details
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine,
Ramathibodi Hospital, Mahidol University, 270 Rama VI Rd., Ratchathewi,
Bangkok 10400, Thailand. 2Department of Pharmacology, College of
Pharmacy, Rangsit University, Pathum Thani, Thailand. 3Social and
Administrative Pharmacy Excellence Research (SAPER) Unit, Department ofPharmacy, Faculty of Pharmacy, Mahidol University, Bangkok, Thailand.
4
Centre for Clincial Epidemiology and Biostatistics, School of Medicine andPublic Health, Faculty of Health and Medicine, University of Newcastle, andHunter Medical Research Institute, Newcastle, NSW, Australia.
1Lukkunaprasit et al. BMC Medical Genetics(2020) 21:210

Received: 16 August 2020 Accepted: 13 October 2020

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